Select shifted column values based on a different column conditional in pandas

Question

I have a dataframe:

I would like to select shifted values on the A column based on a certain condition on the B column, so for instance if B >= 0.20 and my desired shift step is 2 I would get 100 (from B =1.00) and 50 (from B = 0.20 ), rows where B < 0.20 can be discarded.

Can you give a bit more context? Do you have multiple shift values you need to implement in some sort of if-else logic? Or are we just leaving anything <0.2 NaN? — ALollz
– ALollz, Commented May 28, 2019 at 1:11
@ALollz: Not interested in those rows, they could be NaN's or left out ( but I can do that later on a new df I suppose), edited the question, thx. — Keno
– Keno, Commented May 28, 2019 at 1:20

Erfan · Accepted Answer · 2019-05-28 01:23:32Z

1

You can use shift with np.where to conditionally get the shifted values:

shiftstep = 2

df['shift'] = np.where(df['B'].ge(0.2), df['A'].shift(shiftstep), np.NaN)

     A    B  shift
0  100  0.0    NaN
1   50 -0.5    NaN
2  100  1.0  100.0
3  120  0.2   50.0

answered May 28, 2019 at 1:23

Erfan

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2 Comments

Keno Over a year ago

Works like a charm, I wasn't aware of the ge operator...pandas.pydata.org/pandas-docs/stable/reference/api/…

Keno Over a year ago

Note: It turns out that I also needed the opposite selection, using a negative'shiftstep works !

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